1. Field of the Invention
This invention pertains to methods of combining multiple types of information in computer-aided detection (CAD) systems.
2. Description of the Prior Art
Mammographic CAD systems have existed in research environments since the 1960's. Currently available systems find indicators of cancer in mammograms. Typical indicators are clusters of microcalcifications, masses, and spiculated masses. Each indicator may be detected with a specific processing channel. Each channel provides a set of detected regions considered as potentially cancerous. Various methods have been described for combining sets of detections from multiple channels.
The issue of combining multiple detectors in a CAD system has been addressed in prior US patents. Rogers et al, in U.S. application Ser. No. 09/602,762 describe a method for combining outputs of microcalcification and density channels. Case Based Ranking (CBR) limits the total number of marks in a case by retaining only the “best” subset. Additional limits are applied to the total number of marks per image and the total number of each type of mark allowed on any one image of the case. In application Ser. No. 09/602,762, the ranking is based on a difference of discriminants score, which is proportional to the probability of cancer.
Roehrig et al, in U.S. Pat. No. 6,198,838 describe a method for combining outputs of two independent channels: mass detector and spiculation detector. Each detector processes the image, detects regions of interest (ROIs), and computes sets of mass and spiculation feature vectors characterizing the ROI. The combining method taught by Roehrig consists of concatenating mass and spiculation information into a feature vector, then applying it to a classifier. ROIs passing the classifier are then displayed as final detections.
In Roehrig et al, the simple concatenation of features from distinct channels has one very undesirable effect. The probability distributions of the cancer/not cancer concatenated feature are more confusable than either of the original feature vectors. This is because both spiculated and non-spiculated lesions are represented by a single feature vector consisting of mass and spiculation elements. The features specific for spiculatedness will be “noiselike” for regions of interest containing masses. Similarly, the massness features will be noiselike for regions containing spiculations. Assuming an equal number of massness and spiculatedness features, half of the features for any lesion will be noise. The end result is a reduction in classifier accuracy in a fielded system.
Viewed from a different perspective, the classifier is forced to consider masses and spiculated masses as a single “cancer” category. Since feature vectors derived from independent and possibly orthogonal sources, the values of feature vectors in the “cancer” category are dispersed over a larger volume of feature space than would be required if the separate classifiers were applied for the mass and spiculated mass channels.
To achieve higher classification accuracy in CAD systems, there is clearly a need for an improved method to combine lesion information.